demand_level = q0t[q0]
for solar_index in range(len(availability_levels_solar)):##4 levels of solar availability.
avail_solar = availability_levels_solar[solar_index]
for wind_index in range(len(availability_levels_wind)):
avail_wind = availability_levels_wind[wind_index]
allocated_hours = q0_hr_slice[q0][solar_index][wind_index] ## hours in current slice.
# =======================================================
#print('\n' +'-----new slice: '+ str(allocated_hours) + '(hours)----- ')
#print('the demand reference q0 is: ' + str(demand_level))
#print('The availability of solar is : ' + str (avail_solar))
#print('The availability of wind is : ' + str (avail_wind))
# ~ print(df_pp)
df_pp['capacity_available_KW'] = df_pp.apply(lambda row: fun_availability(row.plant_type,avail_solar,avail_wind),axis=1) * df_pp.capacity
ranked_dispatch = fun_rank_dispatch(df_pp)
eq_production,eq_price,last_supply_type,last_supply_percent = fun_demand_supply(df_pp,ranked_dispatch,demand_level)
df_pp['utilization'] = df_pp.apply(lambda row: fun_pp_utilization(row.marginal_cost,last_supply_percent,eq_price),axis=1)
df_pp['dispatched_KWHs'] = df_pp.utilization * df_pp.capacity_available_KW * allocated_hours
#print(df_pp_pi)
df_pp['dispatched_yr'] += df_pp.dispatched_KWHs
continue
continue
continue
yr_dispatch = df_pp[['plant_type','dispatched_yr']].set_index('plant_type')
dispatched_yr_series[str(year)] = pd.concat([yr_dispatch], axis=1, sort=False)
def fun_decision_making(df_pp, carbon_tax, r):
# ~ candidate_pp = pd.DataFrame(columns = ['name','plant_type','capacity','running_cost','investment_cost','total_lifetime','lifetime_remain','emission_intensity','profit_index'])
candidate_pp = {
'CN_baseload': 0,
'coal': 0,
'gas': 0,
'solar': 0,
'wind': 0
} ## set NPV default value as 0.
for pp in pp_choice.values(): ##investgate each type of plant.
new_pp = deepcopy(pp) ##set up new df for new pp.
new_pp[
'marginal_cost'] = new_pp.running_cost + carbon_tax * new_pp.emission_intensity
# =============================================================================
##copy the current supply system df.
df_pp_new = deepcopy(df_pp)
##group the pp by plant type.
df_pp_grouped = df_pp_new.groupby('plant_type', as_index=False).agg({
'capacity':
'sum',
'marginal_cost':
'mean',
'emission_intensity':
'mean'
})
mask = df_pp_grouped.plant_type == new_pp.plant_type.item(
) ##condition.
## add capacity of the new_pp to existing df.
df_pp_grouped.loc[mask, 'capacity'] += new_pp.capacity.item()
# =============================================================================
##check whether biofuel is available.
if biomass in fuel_list:
if df_pp_grouped.loc[
df_pp_grouped.plant_type ==
'gas'].marginal_cost.values > biomass.running_cost: ##if marginal cost of natural gas(default) is bigger than biomass.
index_gas = df_pp_grouped.loc[df_pp_grouped.plant_type ==
'gas'].index
df_pp_grouped.at[
index_gas,
'marginal_cost'] = biomass.running_cost ## switch to biomass.
if new_pp.plant_type.item() == 'gas' and new_pp.marginal_cost.item(
) > biomass.running_cost: ## replace natural gas with biogas
new_pp['marginal_cost'] = biomass.running_cost
# =============================================================================
##calculate for each time slice:
acc_profit_year = 0 ##accumulative profit.
for slice_nr in range(64): ##length is 4, loop through.
demand_level = demand_64[slice_nr]
avail_solar = solar_level[slice_nr]
avail_wind = wind_level[slice_nr]
allocated_hours = slice_hrs[slice_nr] ## hours in current slice.
# =======================================================
# print('\n' +'-----new slice: '+ str(allocated_hours) + '(hours)----- ')
# print('\n' +'-----the slice is: '+ str(time_slice))
#
# print('the demand reference q0 is: ' + str(demand_level))
# print('The availability of solar is : ' + str (avail_solar))
# print('The availability of wind is : ' + str (avail_wind))
# =========================================================
# df_pp_grouped['capacity_available_KW'] = df_pp_grouped.apply(lambda row:
# fun_availability(row.plant_type,avail_solar,avail_wind), axis=1) * df_pp_grouped.capacity
availability_index = [1, 1, 1, avail_solar,
avail_wind] ##CN,coal,gas,solar,wind.
df_pp_grouped[
'capacity_available_KW'] = df_pp_grouped.capacity * availability_index
new_pp['capacity_available_KW'] = fun_availability(
new_pp.plant_type.item(), avail_solar,
avail_wind) * new_pp.capacity.item()
ranked_dispatch = fun_rank_dispatch(df_pp_grouped)
eq_production, eq_price, last_supply_type, last_supply_percent = fun_demand_supply(
df_pp_grouped, ranked_dispatch, demand_level)
new_pp['utilization'] = fun_pp_utilization(
new_pp.marginal_cost.item(), last_supply_percent, eq_price)
##dispatched_KW = pp_utilization * capacity_avail
new_pp[
'dispatched_KW'] = new_pp.utilization * new_pp.capacity_available_KW
new_pp['hr_profit'] = new_pp.dispatched_KW * (
eq_price - new_pp.marginal_cost.item())
new_pp['slice_profit'] = new_pp['hr_profit'] * allocated_hours
# ~ if new_pp.plant_type.item() == 'solar' and avail_solar == 0: ##in this case, solar is not in the df.
# ~ new_plant_slice_profit = 0
#else:
#new_plant_slice_profit = new_pp['slice_profit']
#print('***//////')
#print(new_plant_slice_profit)
acc_profit_year += new_pp['slice_profit'].item(
) ##accumulate profit from all time-slices.
# ~ print('\n' + 'The slice_profit_new_plant is : ' + str (new_plant.slice_profit))
continue
# =============================================================================
##calculate NPV: geometric sequence summation.
NPV_profit = acc_profit_year * (
1 - (1 - r)
**new_pp.total_lifetime.item()) / r - new_pp.investment_cost.item(
) * new_pp.capacity.item() ##NPV minus investment_cost
# ~ print('the acc_profit_year of this new ' + str(new_pp.plant_type.item())+ ' is ' + str(NPV_profit))
if NPV_profit > 0:
lifetime = new_pp.total_lifetime.item()
CRF_pp = r * (1 + r)**lifetime / ((1 + r)**lifetime - 1)
# ~ print(CRF_pp)
profit_index = CRF_pp * NPV_profit / (
new_pp.investment_cost.item() * new_pp.capacity.item())
# ~ print('the profit_index of this new ' + str(new_pp.plant_type.item())+ ' is ' + str(profit_index))
# ~ pdb.set_trace()
# ~ candidate_pp = candidate_pp.append(new_pp,sort=False) ##time= 0.001
candidate_pp[new_pp.plant_type.item()] = profit_index
#print('the NPV_profit and profit_index of this new ' + str(name_new)+ ' plant are ')
#print(NPV_profit,profit_index)
# else:
# print('It is not profitable to invest in ' + str(name_new)+ ' plant.')
# =============================================================================
top_pp = max(candidate_pp, key=lambda key: candidate_pp[key]
) ##return the key with highest value(NPV).
if candidate_pp[top_pp] == 0: ##NPV is zero.
invest_made = None
else:
invest_made = pp_choice[top_pp]
return invest_made
def fun_decision_making(ts, rounds, df_pp, carbon_tax):
print('--------investment process.------------')
# ~ candidate_pp = pd.DataFrame(columns = ['name','plant_type','capacity','running_cost','investment_cost','total_lifetime','lifetime_remain','emission_intensity','profit_index'])
candidate_pp = {
'CN_baseload': 0,
'coal': 0,
'gas': 0,
'solar': 0,
'wind': 0
} ## set NPV default value as 0.
for new_pp in pp_choice.values(): ##investgate each type of plant.
acc_profit_year = 0
# =============================================================================
##set up new df for new pp.
# name_new = str(pp_new.plant_type) +'_t'+str(ts) +'_r'+ str(rounds)##name the plant (unique).
# type_new = pp_new.plant_type
# capacity_new = pp_new.capacity
# running_cost_new = pp_new.running_cost
# investment_cost_new = pp_new.investment_cost
# tot_life_new = pp_new.lifetime
#
# life_remain_new = pp_new.lifetime
# emission_new = pp_new.emission_intensity
# marginal_cost_new = pp_new.running_cost + carbon_tax * pp_new.emission_intensity
# new_pp_params = [name_new,pp_new.plant_type,pp_new.capacity,pp_new.running_cost,pp_new.investment_cost,
# pp_new.lifetime,pp_new.lifetime,pp_new.emission_intensity,marginal_cost_new]
new_pp[
'marginal_cost'] = new_pp.running_cost + carbon_tax * new_pp.emission_intensity
# ~ if new_pp.plant_type.item() == 'gas' and new_pp.marginal_cost.item()> biomass.running_cost: ## replace natural gas with biogas.
# ~ new_pp['marginal_cost'] = biomass.running_cost
# new_pp = pd.DataFrame([new_pp_params],columns=df_pp.columns)
##copy the current energy system df.
df_pp_new = deepcopy(df_pp)
##append new pp to df.
# df_pp_new = df_pp_new.append(new_pp,ignore_index=True,sort=True)## append new pp to df.
# ~ print(df_pp_new[['plant_type','lifetime_remain']])
# print(system_pp_new)
df_pp_grouped = df_pp_new.groupby('plant_type', as_index=False).agg({
'capacity':
'sum',
'marginal_cost':
'mean',
'emission_intensity':
'mean'
})
mask = df_pp_grouped.plant_type == new_pp.plant_type.item(
) ##condition.
## add capacity of the new_pp to existing df.
df_pp_grouped.loc[mask, 'capacity'] += new_pp.capacity.item()
# ~ if df_pp_grouped.loc[df_pp_grouped.plant_type == 'gas'].marginal_cost.values>biomass.running_cost: ##if marginal cost of natural gas(default) is bigger than biomass.
# ~ index_gas = df_pp_grouped.loc[df_pp_grouped.plant_type == 'gas'].index
# ~ df_pp_grouped.at[index_gas, 'marginal_cost'] = biomass.running_cost## switch to biomass.
# =============================================================================
##calculate for each time slice:
for slice_nr in range(64): ##length is 4, loop through.
demand_level = demand_64[slice_nr]
avail_solar = solar_level[slice_nr]
avail_wind = wind_level[slice_nr]
allocated_hours = slice_hrs[slice_nr] ## hours in current slice.
# =======================================================
# print('\n' +'-----new slice: '+ str(allocated_hours) + '(hours)----- ')
# print('\n' +'-----the slice is: '+ str(time_slice))
#
# print('the demand reference q0 is: ' + str(demand_level))
# print('The availability of solar is : ' + str (avail_solar))
# print('The availability of wind is : ' + str (avail_wind))
# =========================================================
# t1_start = time.perf_counter()
# print(df_pp_new[['plant_type','capacity']])
df_pp_grouped['capacity_available_KW'] = df_pp_grouped.apply(
lambda row: fun_availability(row.plant_type, avail_solar,
avail_wind),
axis=1) * df_pp_grouped.capacity
# ~ availability_index = [1,1,1,avail_solar,avail_wind] ##CN,coal,gas,solar,wind.
# ~ print(df_pp_grouped)
# ~ df_pp_grouped['capacity_available_KW'] = df_pp_grouped.capacity * availability_index
new_pp['capacity_available_KW'] = fun_availability(
new_pp.plant_type.item(), avail_solar,
avail_wind) * new_pp.capacity.item()
ranked_dispatch = fun_rank_dispatch(df_pp_grouped)
eq_production, eq_price, last_supply_type, last_supply_percent = fun_demand_supply(
df_pp_grouped, ranked_dispatch, demand_level)
new_pp['utilization'] = fun_pp_utilization(
new_pp.marginal_cost.item(), last_supply_percent, eq_price)
#dispatched_KW = pp_utilization * capacit_avail
new_pp[
'dispatched_KW'] = new_pp.utilization * new_pp.capacity_available_KW
new_pp['hr_profit'] = new_pp.dispatched_KW * (
eq_price - new_pp.marginal_cost.item())
new_pp['slice_profit'] = new_pp['hr_profit'] * allocated_hours
if new_pp.plant_type.item(
) == 'solar' and avail_solar == 0: ##in this case, solar is not in the df.
new_plant_slice_profit = 0
#else:
#new_plant_slice_profit = new_pp['slice_profit']
#print('***//////')
#print(new_plant_slice_profit)
acc_profit_year += new_pp['slice_profit'].item(
) ##accumulate profit from all time-slices.
# =============================================================================
# ~ print('\n' + 'The slice_profit_new_plant is : ' + str (new_plant_slice_profit))
continue
#df_pp_grouped['capacity'].loc[df_pp_grouped.plant_type==type_new]-= capacity_new
##NPV: geometric sequence summation.
NPV_profit = acc_profit_year * (
1 - (1 - r)
**new_pp.total_lifetime.item()) / r - new_pp.investment_cost.item(
) * new_pp.capacity.item() ##NPV minus investment_cost
# ~ print('the acc_profit_year of this new ' + str(new_pp.plant_type.item())+ ' is ' + str(NPV_profit))
if NPV_profit > 0:
CRF_pp = CRF[new_pp.plant_type.item()]
profit_index = CRF_pp * NPV_profit / (
new_pp.investment_cost.item() * new_pp.capacity.item())
# ~ print('the profit_index of this new ' + str(new_pp.plant_type.item())+ ' is ' + str(profit_index))
# ~ new_pp['profit_index'] = profit_index## define profit index.
# ~ candidate_pp = candidate_pp.append(new_pp,sort=False) ##time= 0.001
candidate_pp[new_pp.plant_type.item()] = profit_index
#print('the NPV_profit and profit_index of this new ' + str(name_new)+ ' plant are ')
#print(NPV_profit,profit_index)
# else:
# print('It is not profitable to invest in ' + str(name_new)+ ' plant.')
# =============================================================================
top_pp = max(candidate_pp, key=lambda key: candidate_pp[key]
) ##return the key with highest value(NPV).
if candidate_pp[top_pp] == 0:
invest_made = None
else:
invest_made = pp_choice[top_pp]
# ~ if candidate_pp.empty == False:
# ~ candidate_pp.sort_values(by=['profit_index'], axis=0, ascending=False, inplace=True) ## rank candidates by NPV profit.
# ~ candidate_pp.reset_index(drop=True,inplace=True)
# ~ invest_made = candidate_pp.iloc[0][['name','plant_type','capacity',
# ~ 'running_cost','investment_cost','total_lifetime','lifetime_remain','emission_intensity']]## select the first row-plant with the highest profit index.
# ~ # print(invest_made)
# ~ else:
# ~ invest_made = None
# print('One ' + str(invest_made) + ' is invested by ' + ' company ' + '.')
#
return invest_made